Tensorflow - Convolutonal Neural Network With Custom Data Set Does Not Learn

I am trying to make a convolutional neural network for custom data set. The classifier has only two classes. I am able to read the input images properly and have also assigned them

Solution 1:

Your network has some design flaws. Because of mathematical issues it is not a good idea to calculate the cross entropy yourself and apply a softmax on the output layer. If you are interested in the mathematics I could add this, if not stick to the Tensorflow explanation and method: tf.nn.softmax_cross_entropy_with_logits.

Have you already tried many different configurations? Depending on the complexity of your images a higher or lower kernel size and amount of feature maps might be a good idea. Generally, if your images are relatively homogeneous, a lot of rather similar information is added up and therefore the network has it harder to converge if you have many feature maps. Since you have only two output neurons I assume are images are not very complex?

The next thing is your dropout. You are always using a dropout of 0.5 but normally, for test/validation (like your accuracy prediction) you do not use dropout. In most cases you only use it for training. You can create a placeholder specifying your dropout rate and feed this sess.run.

Here is some example of my own:

h_fc_drop=tf.nn.dropout(h_fc,keep_prob)(...)accu,top1,top3,top5=sess.run([accuracy,te_top1,te_top3,te_top5],feed_dict={x:teX[i:i+batch_size],y:teY[i:i+batch_size]keep_prob:1.0})

This lets Tensorflow calculate my equations for accuracy and topX error rate, while I feed in the test data input teX and the real labels teY for the output with a keep probability keep_prob of 1.0 for the dropout.

Despite this the initialization of your weights is really important in deep neural networks. Even if your design is sufficient for your kind of problem (this also has to be investigated) your network could refuse to learn, diverge or converge to 0 if your weights are not initialized properly. You did not add details to your initialization so you maybe want to look up Xavier initialization. This is an easy beginning for Xavier initialization.

Conclusively I can just encourage you to plot some weights, feature maps, the output over time and so on to get an idea of what your network is doing. Normally this helps a lot.